





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase		:  str			       =logging.get_logger(__name__)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Union[str, Any] 						=      SwinConfig.from_pretrained(
		    'microsoft/swin-tiny-patch4-window7-224'							,out_features=['stage1', 'stage2', 'stage3', 'stage4']						)
		lowerCamelCase_	:     Dict 						=      MaskFormerConfig(backbone_config=_lowerCamelCase						)

		lowerCamelCase_	:     Optional[int] 						=      "huggingface/label-files"
		if "ade20k-full" in model_name:
				# this should be ok
				lowerCamelCase_	:     str 						=      8_47
				lowerCamelCase_	:     List[Any] 						=      "maskformer-ade20k-full-id2label.json"
		elif "ade" in model_name:
				# this should be ok
				lowerCamelCase_	:     Optional[Any] 						=      1_50
				lowerCamelCase_	:     Any 						=      "ade20k-id2label.json"
		elif "coco-stuff" in model_name:
				# this should be ok
				lowerCamelCase_	:     List[Any] 						=      1_71
				lowerCamelCase_	:     List[Any] 						=      "maskformer-coco-stuff-id2label.json"
		elif "coco" in model_name:
				# TODO
				lowerCamelCase_	:     int 						=      1_33
				lowerCamelCase_	:     int 						=      "coco-panoptic-id2label.json"
		elif "cityscapes" in model_name:
				# this should be ok
				lowerCamelCase_	:     Optional[Any] 						=      19
				lowerCamelCase_	:     str 						=      "cityscapes-id2label.json"
		elif "vistas" in model_name:
				# this should be ok
				lowerCamelCase_	:     Tuple 						=      65
				lowerCamelCase_	:     Dict 						=      "mapillary-vistas-id2label.json"

		lowerCamelCase_	:     Optional[int] 						=      json.load(open(hf_hub_download(_lowerCamelCase							,_lowerCamelCase							,repo_type='dataset'						)							,'r'						)						)
		lowerCamelCase_	:     List[Any] 						=      {int(_lowerCamelCase						): v for k, v in idalabel.items()}

		return config


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     str 						=      []
		# stem
		# fmt: off
		rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')						)
		rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')						)
		rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')						)
		rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')						)
		# stages
		for i in range(len(config.backbone_config.depths						)						):
				for j in range(config.backbone_config.depths[i]						):
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")						)
						rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")						)

				if i < 3:
						rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")						)
						rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")						)
						rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")						)
				rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")						)
				rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")						)

		# FPN
		rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')						)
		rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')						)
		rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')						)
		for source_index, target_index in zip(range(3							,0							,-1						)							,range(0							,3						)						):
				rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")						)
				rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")						)
				rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")						)
				rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")						)
				rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")						)
				rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")						)
		rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')						)
		rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')						)

		# Transformer decoder
		for idx in range(config.decoder_config.decoder_layers						):
				# self-attention out projection
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")						)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")						)
				# cross-attention out projection
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")						)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")						)
				# MLP 1
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight")						)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias")						)
				# MLP 2
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight")						)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias")						)
				# layernorm 1 (self-attention layernorm)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")						)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")						)
				# layernorm 2 (cross-attention layernorm)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")						)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")						)
				# layernorm 3 (final layernorm)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")						)
				rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")						)

		rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')						)
		rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')						)

		# heads on top
		rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')						)

		rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')						)
		rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')						)

		rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')						)
		rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')						)

		for i in range(3						):
				rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight")						)
				rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias")						)
		# fmt: on

		return rename_keys


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     Union[str, Any] 						=      dct.pop(_lowerCamelCase						)
		lowerCamelCase_	:     int 						=      val


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     str 						=      [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
		for i in range(len(backbone_config.depths						)						):
				lowerCamelCase_	:     Any 						=      num_features[i]
				for j in range(backbone_config.depths[i]						):
						# fmt: off
						# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
						lowerCamelCase_	:     Union[str, Any] 						=      state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"						)
						lowerCamelCase_	:     List[str] 						=      state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"						)
						# next, add query, keys and values (in that order) to the state dict
						lowerCamelCase_	:     int 						=      in_proj_weight[:dim, :]
						lowerCamelCase_	:     List[str] 						=      in_proj_bias[: dim]
						lowerCamelCase_	:     List[str] 						=      in_proj_weight[
						    dim : dim * 2, :
						]
						lowerCamelCase_	:     Tuple 						=      in_proj_bias[
						    dim : dim * 2
						]
						lowerCamelCase_	:     List[Any] 						=      in_proj_weight[
						    -dim :, :
						]
						lowerCamelCase_	:     Optional[Any] 						=      in_proj_bias[-dim :]
						# fmt: on


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__						):
		lowerCamelCase_	:     Tuple 						=      config.decoder_config.hidden_size
		for idx in range(config.decoder_config.decoder_layers						):
				# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
				lowerCamelCase_	:     str 						=      state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"						)
				lowerCamelCase_	:     List[Any] 						=      state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"						)
				# next, add query, keys and values (in that order) to the state dict
				lowerCamelCase_	:     Optional[Any] 						=      in_proj_weight[: hidden_size, :]
				lowerCamelCase_	:     int 						=      in_proj_bias[:config.hidden_size]
				lowerCamelCase_	:     Any 						=      in_proj_weight[hidden_size : hidden_size * 2, :]
				lowerCamelCase_	:     Optional[int] 						=      in_proj_bias[hidden_size : hidden_size * 2]
				lowerCamelCase_	:     Any 						=      in_proj_weight[-hidden_size :, :]
				lowerCamelCase_	:     Optional[Any] 						=      in_proj_bias[-hidden_size :]
				# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
				lowerCamelCase_	:     Union[str, Any] 						=      state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"						)
				lowerCamelCase_	:     str 						=      state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"						)
				# next, add query, keys and values (in that order) to the state dict
				lowerCamelCase_	:     str 						=      in_proj_weight[: hidden_size, :]
				lowerCamelCase_	:     Optional[int] 						=      in_proj_bias[:config.hidden_size]
				lowerCamelCase_	:     List[str] 						=      in_proj_weight[hidden_size : hidden_size * 2, :]
				lowerCamelCase_	:     Dict 						=      in_proj_bias[hidden_size : hidden_size * 2]
				lowerCamelCase_	:     Any 						=      in_proj_weight[-hidden_size :, :]
				lowerCamelCase_	:     int 						=      in_proj_bias[-hidden_size :]
		# fmt: on


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     int 						=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCamelCase_	:     Optional[Any] 						=      Image.open(requests.get(_lowerCamelCase							,stream=_lowerCamelCase						).raw						)
		return im


@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__ = False						):
		lowerCamelCase_	:     List[str] 						=      get_maskformer_config(_lowerCamelCase						)

		# load original state_dict
		with open(_lowerCamelCase							,'rb'						) as f:
				lowerCamelCase_	:     str 						=      pickle.load(_lowerCamelCase						)
		lowerCamelCase_	:     Union[str, Any] 						=      data["model"]

		# for name, param in state_dict.items():
		#     print(name, param.shape)

		# rename keys
		lowerCamelCase_	:     Dict 						=      create_rename_keys(_lowerCamelCase						)
		for src, dest in rename_keys:
				rename_key(_lowerCamelCase							,_lowerCamelCase							,_lowerCamelCase						)
		read_in_swin_q_k_v(_lowerCamelCase							,config.backbone_config						)
		read_in_decoder_q_k_v(_lowerCamelCase							,_lowerCamelCase						)

		# update to torch tensors
		for key, value in state_dict.items():
				lowerCamelCase_	:     Dict 						=      torch.from_numpy(_lowerCamelCase						)

		# load 🤗 model
		lowerCamelCase_	:     List[Any] 						=      MaskFormerForInstanceSegmentation(_lowerCamelCase						)
		model.eval()

		for name, param in model.named_parameters():
				print(_lowerCamelCase							,param.shape						)

		lowerCamelCase_	:     Optional[Any] 						=      model.load_state_dict(_lowerCamelCase							,strict=_lowerCamelCase						)
		assert missing_keys == [
		    "model.pixel_level_module.encoder.model.layernorm.weight",
		    "model.pixel_level_module.encoder.model.layernorm.bias",
		]
		assert len(_lowerCamelCase						) == 0, F"Unexpected keys: {unexpected_keys}"

		# verify results
		lowerCamelCase_	:     Any 						=      prepare_img()
		if "vistas" in model_name:
				lowerCamelCase_	:     str 						=      65
		elif "cityscapes" in model_name:
				lowerCamelCase_	:     Any 						=      6_55_35
		else:
				lowerCamelCase_	:     Any 						=      2_55
		lowerCamelCase_	:     List[str] 						=      True if "ade" in model_name else False
		lowerCamelCase_	:     Dict 						=      MaskFormerImageProcessor(ignore_index=_lowerCamelCase							,reduce_labels=_lowerCamelCase						)

		lowerCamelCase_	:     Optional[Any] 						=      image_processor(_lowerCamelCase							,return_tensors='pt'						)

		lowerCamelCase_	:     Union[str, Any] 						=      model(**_lowerCamelCase						)

		print('Logits:'							,outputs.class_queries_logits[0, :3, :3]						)

		if model_name == "maskformer-swin-tiny-ade":
				lowerCamelCase_	:     Tuple 						=      torch.tensor(
				    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]						)
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3]							,_lowerCamelCase							,atol=1e-4						)
		print('Looks ok!'						)

		if pytorch_dump_folder_path is not None:
				print(F"Saving model and image processor to {pytorch_dump_folder_path}"						)
				Path(_lowerCamelCase						).mkdir(exist_ok=_lowerCamelCase						)
				model.save_pretrained(_lowerCamelCase						)
				image_processor.save_pretrained(_lowerCamelCase						)

		if push_to_hub:
				print('Pushing model and image processor to the hub...'						)
				model.push_to_hub(F"nielsr/{model_name}"						)
				image_processor.push_to_hub(F"nielsr/{model_name}"						)


if __name__ == "__main__":
		_lowercase		:  int			       =argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--model_name""",
		    default="""maskformer-swin-tiny-ade""",
		    type=str,
		    help=("""Name of the MaskFormer model you\'d like to convert""",),
		)
		parser.add_argument(
		    """--checkpoint_path""",
		    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
		    type=str,
		    help="""Path to the original state dict (.pth file).""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		_lowercase		:  Tuple			       =parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :   int						=			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   List[str]						=			['ConditionalDetrFeatureExtractor']
		lowercase    :   int						=			['ConditionalDetrImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   Optional[int]						=			[
		    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ConditionalDetrForObjectDetection',
		    'ConditionalDetrForSegmentation',
		    'ConditionalDetrModel',
		    'ConditionalDetrPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		lowercase    :   Optional[int]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)